Hybrid Heat Transfer Search and Passing Vehicle Search optimizer for multi-objective structural optimization

A novel hybrid optimizer called Multi-Objective Hybrid Heat Transfer Search and Passing Vehicle Search optimizer (MOHHTS-PVS) is proposed while its performance is investigated for the structural design. The HHTS-PVS optimizer combines the merits of Heat Transfer Search (HTS) and Passing Vehicle Search (PVS). The design problem is posed for weight minimization and maximization of nodal deflection subject to multiple constraints of trusses. In the proposed optimizer, HTS acts as the main engine and PVS is added as an auxiliary stage into it to overcome its limitations and enhance the performance while simultaneously creating harmony between global diversification and local intensification of the search. Five challenging structure optimization benchmarks are optimized having discrete design variables. For performance validation, four state-of-the-art optimizers are compared with the proposed optimizer. Pareto Front Hypervolume and Spacing-to-Extent test are performance indicators for all the test examples. HHTS-PVS achieved the best non-dominated Pareto fronts with continuous and well diverse solutions set. The statistical analysis is done by performing Friedman's rank test and allocating respective ranks to the optimizers. As per the outcomes, it is concluded that HHTS-PVS outperforms other optimizers and simultaneously shows its competency in solving large engineering design problems. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

A novel hybrid optimizer combining Heat Transfer Search (HTS) and Passing Vehicle Search (PVS) methods is proposed for weight minimization and nodal deflection maximization in structural design. The optimizer achieves a balance between global diversification and local intensification by adding PVS as an auxiliary stage into the main engine of HTS to enhance performance.